Date: 07/25/2022 Georgia Tech Data Boot Camp 2022
Brian Hall, Daulton Davis, Hassan Mohamed, Joseph Johnson, Kelly Brown, Leon Lee, Orlvertta Moody, Valentina Zhu, and Vedrana Basimamovic
Netflix is one of top streaming services which offers a wide variety of award-winning TV shows, movies, original content and much more. Upper managements is concerned with a declining trend in subscriber count, and is seeking answers on trends among four genres: Action, Horror, Documentary, and Comedy.
We have been tasked with current content analysis which will aid the management in developing original content that would be attractive to current and future subscribers, and revers the declining subscription rates.
Group analysis will explore Netflix dataset through visualizations and graphs using python libraries and present trends among four genres using the following data sets:
- https://www.kaggle.com/datasets/shivamb/netflix-shows
- https://www.kaggle.com/datasets/victorsoeiro/netflix-tv-shows-and-movies
- https://developers.themoviedb.org/
Further, we will review the following:
- Quantity of each genre (number of movies and shows)
- Quality of each genre (IMDb average rating)
- Which movie was the most popular for Netflix for the specific genre (TMDB)
- Which actor/actress was most featured for the specific genre.
Team will prepare a formal 10-minute presentation that covers the following topics:
- Questions that you found interesting and what motivated you to answer them
- Where and how we found the data we used to answer these questions
- The data exploration and cleanup process (accompanied by your Jupyter notebook)
- The analysis process (accompanied by your Jupyter notebook)
- Our conclusions, including a numerical summary and visualizations of the summary
- The implications of our findings: what do our findings mean?
o Use Pandas to clean and format your dataset or datasets.
o Create a Jupyter notebook describing the data exploration and cleanup process.
o Create a Jupyter notebook illustrating the final data analysis.
o Use Matplotlib to create 6 to 8 visualizations of your data (ideally, at least 2 visualizations per “question” that you ask your data).
o Save PNG images of your visualizations to distribute to the class and instructional team—and for inclusion in your presentation.
o Create a write-up summarizing your major findings. This should include a heading for each “question” that you asked your data as well as a short description of your findings and any relevant plots.
- Bonus: Use at least one API—if you can find one with data pertinent to your primary research questions.